The fragmentomic property of plasma cell-free DNA enables the non-invasive detection of diabetic nephropathy in patients with diabetes mellitus

Circulating Cell-Free DNA in Metabolic Diseases

Metabolic diseases affect a consistent part of the human population, leading to rising mortality rates. This raises the need for diagnostic tools to monitor the progress of these diseases. Lately, circulating cell-free DNA (cfDNA) has emerged as a promising biomarker for various metabolic diseases, including obesity, type 2 diabetes, and metabolic-associated fatty liver disease. CfDNA is released from apoptotic and necrotic cells into the bloodstream and other body fluids, and it retains various molecular signatures of its tissue of origin. Thus, cfDNA load and composition can reflect tissue pathologies and systemic metabolic dysfunctions. In addition to its potential as a diagnostic biomarker, interest in cfDNA derives from its recently discovered role in adipose tissue inflammation in obesity. This review discusses detection methods and clinical significance of cfDNA in metabolic diseases.

"Aging Clocks" Based on Cell-Free DNA

Aging is associated with systemic changes in the physiological and molecular parameters of the body. These changes are referred to as biomarkers of aging. Statistical models that link changes in individual biomarkers to biological age are called aging clocks. These tools facilitate a comprehensive evaluation of bodily health and permit the quantitative determination of the rate of aging. A particularly promising area for the development of aging clocks is the analysis of cell-free DNA (cfDNA), which is present in the blood and contains numerous potential biomarkers. This review explores in detail the fragmentomics, topology, and epigenetic landscape of cfDNA as possible biomarkers of aging. The review further underscores the potential of leveraging single-molecule sequencing of cfDNA in conjunction with long-read technologies to simultaneously profile multiple biomarkers, a strategy that could lead to the development of more precise aging clocks.

Utilizing machine learning algorithms to identify biomarkers associated with diabetic nephropathy: A review

Diabetic nephropathy (DN), a multifaceted disease with various contributing factors, presents challenges in understanding its underlying causes. Uncovering biomarkers linked to this condition can shed light on its pathogenesis and support the creation of new diagnostic and treatment methods. Gene expression data were sourced from accessible public databases, and Weighted Gene Co-expression Network Analysis (WGCNA)was employed to pinpoint gene co-expression modules relevant to DN. Subsequently, various machine learning techniques, such as random forest, lasso regression algorithm (LASSO), and support vector machine-recursive feature elimination (SVM-REF), were utilized for distinguishing DN cases from controls using the identified gene modules. Additionally, functional enrichment analyses were conducted to explore the biological roles of these genes. Our analysis revealed 131 genes showing distinct expression patterns between controlled and uncontrolled groups. During the integrated WCGNA, we identified 61 co-expressed genes encompassing both categories. The enrichment analysis highlighted involvement in various immune responses and complex activities. Techniques like Random Forest, LASSO, and SVM-REF were applied to pinpoint key hub genes, leading to the identification of VWF and DNASE1L3. In the context of DN, they demonstrated significant consistency in both expression and function. Our research uncovered potential biomarkers for DN through the application of WGCNA and various machine learning methods. The results indicate that 2 central genes could serve as innovative diagnostic indicators and therapeutic targets for this disease. This discovery offers fresh perspectives on the development of DN and could contribute to the advancement of new diagnostic and treatment approaches.